Seurat QC and Object for pbmc_1k_v3

Information about this notebook

This script creates Seurat object from the aligned count data and performs QC based on Seurat functions. miQC is used to filter out low quality cells in a given library.

For more information or updates, please see Seurat.

Usage

To run the Rscript from the command line sequentially, use:

Rscript -e "rmarkdown::render('seurat_alignment_qc.Rmd', clean = TRUE,
      params=list(scooter_path='/scooter', 
                  results_dir='.', 
                  data_path='/path/to/cellranger/output', 
                  sample_name='pbmc_1k_v3',
                  min_genes=200,
                  normalize_method='log_norm',
                  num_pcs=30
                  ))"

Set up

Directories and paths to file Inputs/Outputs

attach(params)

The following objects are masked _by_ .GlobalEnv:

    data_path, results_dir, sample_name

The following objects are masked from params (pos = 6):

    assay, data_path, grouping, log_file, min_genes, normalize_method, num_dim, num_neighbors, num_pcs, prefix, results_dir,
    sample_name, scooter_path

#load_all(scooter_path)
load_all("/Users/chronia/CHOP/GitHub/scooter") # from https://github.com/igordot/scooter

Read in 10x data

# Load counts to a list of matrices
counts_mat <- load_sample_counts_matrix(path = data_path,
                                        sample_name = sample_name)

loading counts matrix for sample: 7316-371
loading counts matrix dir: /Users/chronia/CHOP/GitHub/single-cell-analysis/data/10x-wf/7316-371/7316-371/outs/filtered_feature_bc_matrix


 ========== import cell ranger counts matrix ========== 

# Create seurat object using gene expression data
seurat_obj <- create_seurat_obj(counts_matrix = counts_mat[["Gene Expression"]],
                                assay = "RNA",
                                log_file = NULL) 

========== create seurat object ========== 

                    input cells: 3564
                    input genes: 33940
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')

# Save raw Seurat object
saveRDS(seurat_obj, file = paste0(results_dir, "/", "seurat_obj_raw.rds"))

# How many cells are in the seurat object?
cells_before_filter_num <- ncol(seurat_obj)

There are 3564 cells in the raw seurat object.

Plot data before filter

Plot distribution of the number of genes, UMI, and percent mitochondrial reads per cell.

# Plot number of genes 
genes_unfilt <- plot_distribution(seurat_obj, 
                                  features = "nFeature_RNA", 
                                  grouping = grouping) +
  geom_hline(yintercept = min_genes, color = "#D41159", size = 2) +
  #geom_hline(yintercept = max(seurat_obj@meta.data$nFeature_RNA), color = "#D41159", size = 2) +
  scale_fill_manual(values = alpha(c("#10559a"), .4)) +
  #annotate(geom="text", x=0.7, y = max(seurat_obj@meta.data$nFeature_RNA), label= glue(""), color="#D41159") +
  annotate(geom="text", x=0.7, y = min_genes - 200, label = glue("Min genes: {min_genes}"), color="#D41159")

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
Please use `linewidth` instead.Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.

# plot number of UMIs
umi_unfilt <- plot_distribution(seurat_obj, 
                                features = "nCount_RNA",
                                grouping = grouping) +
  scale_fill_manual(values = alpha(c("#10559a"), .4)) 

Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.

# plot percent mitochondrial reads
mito_unfilt <- plot_distribution(seurat_obj, 
                                 features = "pct_mito",
                                 grouping = grouping) +
  #geom_hline(yintercept = max(seurat_obj@meta.data$pct_mito), color = "#D41159", size = 2) +
  scale_fill_manual(values = alpha(c("#10559a"), .4)) 

Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.

  #annotate(geom="text", x=0.7, y = max(seurat_obj@meta.data$pct_mito), label = glue(""), color="#D41159")

# get the legend for one of the plots to use as legend for the combined plot
legend_grid <- get_legend(mito_unfilt)

title <- ggdraw() + 
  draw_label("Unfiltered-data", fontface = 'bold', x = 0, hjust = 0) +
  theme(
    # add margin on the left of the drawing canvas,
    # so title is aligned with left edge of first plot
    plot.margin = margin(0, 0, 0, 7))

# Combine plots 
plot_row <- plot_grid(genes_unfilt + theme(legend.position = "none"),
                      umi_unfilt + theme(legend.position = "none"),
                      mito_unfilt + theme(legend.position = "none"),
                      legend_grid,
                      ncol = 4)

plot_grid(title, plot_row, ncol = 1, rel_heights = c(0.1, 1))

miQC steps for filtering low quality cells

We will use miQC to identify low-quality cells. miQC R package jointly models proportion of reads belonging to mitochondrial genes and number of unique genes detected. Cells with a high likelihood of being compromised (greater than 0.75) and cells that do not pass a minimum number of unique genes detected threshold of 200 will be removed from the counts matrix object.

Create sce

sce <- as.SingleCellExperiment(seurat_obj)

Scater preprocessing

In order to calculate the percent of reads in a cell that map to mitochondrial genes, we first need to establish which genes are mitochondrial. For genes listed as HGNC symbols, this is as simple as searching for genes starting with mt-. For other IDs, we recommend using a biomaRt query to map to chromosomal location and identify all mitochondrial genes.

# Set seed
set.seed(2023)

# Identify cells with mtDNA in the library
#mt_genes <- grepl("^mt-",  rownames(sce))
mt_genes <- grepl(sce$pct_mito, rownames(sce))

Warning: argument 'pattern' has length > 1 and only the first element will be used

feature_ctrls <- list(mito = rownames(sce)[mt_genes])
feature_ctrls

$mito
   [1] "MIR1302-2HG"     "ENSG00000238009" "ENSG00000268903" "ENSG00000241860" "ENSG00000241599" "ENSG00000228463" "ENSG00000237094"
   [8] "ENSG00000230021" "ENSG00000228327" "LINC01409"       "ENSG00000230092" "LINC01128"       "LINC00115"       "ENSG00000288531"
  [15] "ENSG00000272438" "ENSG00000230699" "ENSG00000241180" "ENSG00000272512" "ENSG00000231702" "ENSG00000217801" "ENSG00000285812"
  [22] "LINC01342"       "TTLL10"          "ENSG00000260179" "LINC01786"       "ENSG00000240731" "MRPL20-AS1"      "MRPL20"         
  [29] "MRPL20-DT"       "LINC01770"       "ENSG00000284740" "TMEM240"         "ENSG00000215014" "FNDC10"          "ENSG00000286989"
  [36] "ENSG00000272106" "ENSG00000272004" "ENSG00000269737" "ENSG00000268575" "ENSG00000227775" "ENSG00000231050" "ENSG00000233542"
  [43] "ENSG00000271806" "FAAP20"          "ENSG00000234396" "ENSG00000287356" "ENSG00000272161" "ENSG00000269896" "ENSG00000272420"
  [50] "PEX10"           "ENSG00000224387" "ENSG00000272449" "ENSG00000285945" "ENSG00000279839" "ENSG00000272235" "ENSG00000287828"
  [57] "ENSG00000286518" "ENSG00000238260" "CEP104"          "LINC01134"       "LINC02780"       "ENSG00000284703" "ENSG00000287586"
  [64] "ENSG00000236948" "ENSG00000285629" "RNF207-AS1"      "RNF207"          "LINC00337"       "ENSG00000231868" "ENSG00000229519"
  [71] "LINC01672"       "ENSG00000284744" "ENSG00000237365" "ENSG00000270171" "ENSG00000270035" "ENSG00000269978" "ENSG00000269925"
  [78] "ENSG00000236266" "ENSG00000284747" "ENSG00000284716" "ENSG00000238290" "ENSG00000232848" "ENSG00000288816" "LINC01714"      
  [85] "ENSG00000226545" "LINC02606"       "ENSG00000231181" "TMEM201"         "ENSG00000233268" "ENSG00000280113" "ENSG00000285701"
  [92] "ENSG00000228150" "ENSG00000284735" "ENSG00000284642" "ENSG00000271989" "ENSG00000203469" "ENSG00000272078" "EXOSC10"        
  [99] "ENSG00000226849" "EXOSC10-AS1"     "ENSG00000284646" "ENSG00000284708" "ENSG00000285646" "KIAA2013"        "LINC02766"      
 [106] "ENSG00000288927" "ENSG00000272482" "ENSG00000237445" "ENSG00000253085" "ENSG00000289380" "ENSG00000231606" "ENSG00000287756"
 [113] "ENSG00000228140" "ENSG00000272510" "ENSG00000237301" "ENSG00000237938" "ENSG00000178715" "ENSG00000234607" "ENSG00000227959"
 [120] "ENSG00000224621" "ENSG00000288398" "LINC01772"       "ENSG00000261135" "ENSG00000285853" "ENSG00000282143" "ENSG00000280114"
 [127] "ENSG00000282740" "ENSG00000238142" "ENSG00000272426" "ENSG00000226526" "LINC02783"       "ARHGEF10L"       "LINC02810"      
 [134] "ENSG00000284653" "ENSG00000280222" "ENSG00000225387" "ENSG00000272084" "ENSG00000286064" "ENSG00000270728" "RNU6-1099P"     
 [141] "MICOS10"         "ENSG00000235185" "ENSG00000226396" "ENSG00000235434" "ENSG00000227066" "UBXN10"          "ENSG00000284710"
 [148] "LINC01141"       "ENSG00000226487" "ENSG00000235432" "ENSG00000287192" "LINC02596"       "ENSG00000283234" "ENSG00000285959"
 [155] "LINC01635"       "LINC00339"       "ENSG00000285873" "ZBTB40"          "ENSG00000240553" "ENSG00000289014" "LINC01355"      
 [162] "ENSG00000284726" "ENSG00000232482" "ENSG00000285802" "ENSG00000229239" "SRSF10"          "ENSG00000225315" "ENSG00000288982"
 [169] "ENSG00000284699" "ENSG00000233755" "ENSG00000284602" "ENSG00000284657" "ENSG00000272432" "ENSG00000261349" "TMEM50A"        
 [176] "ENSG00000225643" "ENSG00000233478" "ENSG00000255054" "ENSG00000228172" "ENSG00000236528" "SLC30A2"         "ENSG00000284309"
 [183] "FAM110D"         "ENSG00000223583" "ENSG00000260063" "ENSG00000289554" "NR0B2"           "ENSG00000224311" "ENSG00000243659"
 [190] "ENSG00000241169" "ENSG00000237429" "LINC02574"       "ENSG00000225886" "ENSG00000231344" "ENSG00000269971" "ENSG00000270031"
 [197] "ENSG00000286433" "ENSG00000227050" "ENSG00000289576" "ENSG00000270605" "ENSG00000229820" "ENSG00000279443" "LINC01715"      
 [204] "ENSG00000233427" "TMEM200B"        "ENSG00000225750" "LINC01756"       "ENSG00000229607" "LINC01778"       "SNRNP40"        
 [211] "ENSG00000229447" "ENSG00000229044" "LINC01226"       "ENSG00000203620" "ENSG00000269967" "ENSG00000284702" "ENSG00000203325"
 [218] "ENSG00000250135" "ENSG00000224066" "ENSG00000233775" "ENSG00000254553" "S100PBP"         "ENSG00000287691" "ENSG00000278966"
 [225] "ENSG00000239670" "ENSG00000236065" "ENSG00000278997" "ENSG00000279179" "ENSG00000284721" "ENSG00000270115" "ENSG00000225313"
 [232] "ZSCAN20"         "ENSG00000235907" "ENSG00000287703" "ENSG00000284773" "ENSG00000271741" "ENSG00000284640" "RN7SL503P"      
 [239] "KIAA0319L"       "ENSG00000236274" "ENSG00000232335" "ENSG00000286899" "ENSG00000271914" "ENSG00000271554" "ENSG00000232862"
 [246] "ENSG00000286379" "STK40"           "LSM10"           "ENSG00000284705" "ENSG00000284650" "ENSG00000223944" "LINC01137"      
 [253] "ENSG00000284748" "C1orf109"        "EPHA10"          "ENSG00000230955" "ENSG00000223589" "ENSG00000286552" "ENSG00000287987"
 [260] "LINC01685"       "ENSG00000284632" "ENSG00000273637" "ENSG00000228436" "ENSG00000274944" "ENSG00000287422" "ENSG00000226438"
 [267] "ENSG00000225333" "ENSG00000261798" "ENSG00000284719" "LINC02811"       "ENSG00000228477" "ENSG00000231296" "ENSG00000213172"
 [274] "ENSG00000284677" "ENSG00000260920" "ENSG00000279667" "ENSG00000238287" "ENSG00000238186" "ENSG00000286838" "ENSG00000287743"
 [281] "ENSG00000237899" "ENSG00000229528" "ENSG00000287400" "ENSG00000287587" "CCDC30"          "ENSG00000236180" "ENSG00000285728"
 [288] "ENSG00000234917" "C1orf50"         "ENSG00000283580" "ENSG00000228192" "ENSG00000283973" "ENSG00000234694" "CDC20"          
 [295] "ENSG00000288772" "ENSG00000284989" "ENSG00000237950" "ENSG00000288573" "ENSG00000285649" "ATP6V0B"         "ENSG00000230615"
 [302] "ENSG00000227163" "RNF220"          "ENSG00000225721" "ENSG00000226499" "LINC01144"       "ENSG00000288208" "ENSG00000289407"
 [309] "ENSG00000281112" "RPS15AP10"       "ENSG00000234329" "ENSG00000230896" "ENSG00000226957" "ENSG00000233114" "ENSG00000227857"
 [316] "LINC00853"       "ENSG00000226252" "LINC01389"       "LINC02794"       "ENSG00000279096" "ENSG00000223720" "ENSG00000272491"
 [323] "ENSG00000279214" "ENSG00000287661" "ENSG00000229846" "ENSG00000237478" "ENSG00000230828" "LINC01562"       "ENSG00000233406"
 [330] "ENSG00000236434" "ENSG00000266993" "ENSG00000272175" "ENSG00000223390" "ANAPC10P1"       "ENSG00000272100" "ENSG00000287078"
 [337] "ENSG00000272371" "ENSG00000230953" "ENSG00000235563" "ENSG00000236723" "ENSG00000226754" "ENSG00000234578" "ENSG00000228838"
 [344] "LINC01771"       "LINC02812"       "ENSG00000280378" "ENSG00000225183" "ENSG00000256407" "ENSG00000225632" "ENSG00000287582"
 [351] "ENSG00000230728" "ENSG00000287724" "ENSG00000242396" "ENSG00000233271" "ENSG00000234810" "LINC01753"       "ENSG00000235612"
 [358] "ENSG00000260971" "ENSG00000284686" "RPSAP20"         "LINC01767"       "ENSG00000227935" "RPS20P5"         "ENSG00000235038"
 [365] "ENSG00000286918" "ENSG00000185839" "ENSG00000283445" "LINC01135"       "LINC02777"       "LINC01358"       "ENSG00000235215"
 [372] "ENSG00000270457" "ENSG00000226883" "LINC01748"       "ENSG00000231252" "ENSG00000287224" "RN7SL180P"       "ENSG00000213703"
 [379] "ENSG00000235545" "LINC01739"       "LINC00466"       "ENSG00000286429" "RN7SL130P"       "LINC01359"       "ENSG00000272506"
 [386] "ENSG00000229294" "ENSG00000248458" "ENSG00000231080" "ENSG00000289394" "ENSG00000275678" "ENSG00000233589" "ENSG00000285407"
 [393] "ENSG00000285473" "LINC01707"       "LINC02791"       "ENSG00000287453" "LRRC40"          "ENSG00000271992" "LINC01788"      
 [400] "ENSG00000269933" "ENSG00000226324" "ENSG00000231985" "ENSG00000286863" "ENSG00000225087" "LINC02796"       "LINC02797"      
 [407] "ENSG00000285778" "ENSG00000237324" "ENSG00000272864" "ENSG00000224493" "RNU6-503P"       "ENSG00000230863" "ENSG00000225605"
 [414] "ENSG00000230027" "ENSG00000272855" "ENSG00000288543" "ENSG00000230498" "ENSG00000228187" "ENSG00000289212" "ENSG00000287870"
 [421] "RN7SL370P"       "ENSG00000273338" "ENSG00000238015" "ENSG00000235400" "ENSG00000288822" "ENSG00000285409" "LINC01781"      
 [428] "ENSG00000285179" "ENSG00000234953" "ENSG00000236676" "ENSG00000233290" "LINC01362"       "LINC01361"       "LINC01725"      
 [435] "ENSG00000229486" "ENSG00000285201" "ENSG00000285851" "ENSG00000284882" "ENSG00000280099" "BCL10"           "ENSG00000223653"
 [442] "ENSG00000282057" "ENSG00000272691" "LINC02795"       "ENSG00000284846" "ENSG00000267734" "ENSG00000267561" "ENSG00000225568"
 [449] "LINC01140"       "LINC01364"       "ENSG00000279778" "ENSG00000286758" "ENSG00000286802" "ENSG00000284734" "ENSG00000233235"
 [456] "ENSG00000286548" "ENSG00000271949" "ENSG00000231613" "ENSG00000272672" "ENSG00000287406" "ENSG00000287372" "LINC02787"      
 [463] "LINC02609"       "LINC02788"       "LINC01763"       "ENSG00000272094" "ENSG00000229067" "ENSG00000289483" "ENSG00000273487"
 [470] "ENSG00000223787" "ENSG00000225505" "ENSG00000229052" "ENSG00000289544" "ENSG00000287797" "ENSG00000237003" "ENSG00000229567"
 [477] "ENSG00000260464" "ENSG00000236098" "ENSG00000288736" "ENSG00000223675" "ENSG00000231992" "ENSG00000226026" "LINC02607"      
 [484] "LINC02790"       "LINC01787"       "ENSG00000230718" "ENSG00000259946" "ENSG00000285922" "LINC01776"       "ENSG00000232825"
 [491] "ENSG00000227034" "LINC01708"       "ENSG00000228084" "ENSG00000283761" "RNU6-750P"       "ENSG00000288826" "ENSG00000241073"
 [498] "ENSG00000285530" "ENSG00000228086" "LINC01349"       "ENSG00000285525" "ENSG00000273204" "SLC30A7"         "ENSG00000235795"
 [505] "ENSG00000233184" "LINC01307"       "LINC01709"       "ENSG00000289192" "ENSG00000233359" "ENSG00000230864" "ENSG00000224613"
 [512] "ENSG00000215869" "ENSG00000285981" "LINC01676"       "ENSG00000232952" "LINC01677"       "ENSG00000237480" "LINC01661"      
 [519] "ENSG00000289612" "LINC02785"       "FAM102B"         "ENSG00000285923" "ENSG00000237349" "ENSG00000251484" "ENSG00000225113"
 [526] "ENSG00000260246" "LINC01768"       "ENSG00000258634" "LINC01397"       "ENSG00000273373" "ENSG00000259834" "ENSG00000288847"
 [533] "ENSG00000288803" "ENSG00000261654" "ENSG00000232811" "ENSG00000273010" "ENSG00000273221" "ENSG00000229283" "ENSG00000260948"
 [540] "ENSG00000243960" "LINC01160"       "ENSG00000284830" "DDX20"           "LINC01750"       "LINC02884"       "ENSG00000273483"
 [547] "MOV10"           "ENSG00000225075" "LINC01356"       "LINC01357"       "ENSG00000287807" "ENSG00000232499" "ENSG00000232450"
 [554] "ENSG00000231128" "ENSG00000232895" "LINC01765"       "ENSG00000237993" "LINC01762"       "ENSG00000224950" "ENSG00000286276"
 [561] "CD101"           "ENSG00000236137" "LINC01525"       "ENSG00000271427" "ENSG00000279513" "ENSG00000227712" "LINC01780"      
 [568] "LINC00622"       "ENSG00000274642" "ENSG00000223804" "ENSG00000287979" "LINC00623"       "ENSG00000234998" "ENSG00000227193"
 [575] "LINC02798"       "ENSG00000272583" "ENSG00000277702" "ENSG00000274927" "ENSG00000223495" "LINC02799"       "ENSG00000232721"
 [582] "ENSG00000223779" "ENSG00000230186" "ENSG00000289318" "LINC02802"       "ENSG00000237343" "ENSG00000233586" "LINC01632"      
 [589] "ENSG00000224363" "ENSG00000283752" "LINC01145"       "ENSG00000276216" "ENSG00000236140" "NBPF20"          "ENSG00000286620"
 [596] "CD160"           "ITGA10"          "ENSG00000289565" "ENSG00000287190" "LINC01719"       "NBPF10"          "ENSG00000276509"
 [603] "ENSG00000287978" "ENSG00000289321" "ENSG00000227242" "ENSG00000237188" "LINC00624"       "ENSG00000289419" "ENSG00000234190"
 [610] "ENSG00000273059" "LINC02804"       "LINC02805"       "ENSG00000227733" "LINC01731"       "LINC01138"       "ENSG00000224481"
 [617] "ENSG00000272824" "ENSG00000225871" "ENSG00000280649" "ENSG00000289642" "ENSG00000255148" "ENSG00000254539" "ENSG00000274265"
 [624] "ENSG00000289614" "ENSG00000215861" "ENSG00000272755" "ENSG00000286185" "LINC00869"       "ENSG00000233030" "H2BC20P"        
 [631] "ENSG00000264207" "H2AC20"          "ENSG00000285184" "ENSG00000223945" "ENSG00000285554" "ENSG00000276110" "ENSG00000289041"
 [638] "ENSG00000289457" "RN7SL600P"       "ENSG00000253047" "ENSG00000288880" "RNU6-1309P"      "ENSG00000261168" "ENSG00000289288"
 [645] "ENSG00000273481" "ENSG00000232536" "ENSG00000223861" "ENSG00000250734" "ENSG00000227045" "ENSG00000269621" "ENSG00000249602"
 [652] "ENSG00000269489" "ENSG00000285651" "S100A10"         "ENSG00000229021" "S100A11"         "ENSG00000236427" "S100A9"         
 [659] "S100A12"         "S100A8"          "S100A6"          "S100A4"          "S100A3"          "S100A2"          "ENSG00000285867"
 [666] "S100A16"         "S100A13"         "ENSG00000271853" "S100A1"          "ENSG00000272030" "ENSG00000243613" "ENSG00000231827"
 [673] "ENSG00000223599" "ENSG00000236327" "ENSG00000284738" "ENSG00000285779" "ENSG00000282386" "ENSG00000273026" "ENSG00000285641"
 [680] "ENSG00000272654" "NUP210L"         "ENSG00000231416" "AQP10"           "TDRD10"          "ENSG00000286391" "ENSG00000287064"
 [687] "ENSG00000270361" "ENSG00000271380" "SLC50A1"         "ENSG00000273088" "ENSG00000236263" "ENSG00000232519" "ENSG00000246203"
 [694] "ENSG00000287839" "ENSG00000227673" "ENSG00000234937" "ENSG00000285677" "ENSG00000252236" "ENSG00000237390" "ENSG00000289593"
 [701] "ENSG00000260460" "ENSG00000272405" "ENSG00000229953" "ENSG00000285570" "ENSG00000223356" "ISG20L2"         "ENSG00000229961"
 [708] "ENSG00000236957" "LINC01704"       "ENSG00000176320" "ENSG00000228560" "ENSG00000289484" "ENSG00000256029" "ENSG00000272668"
 [715] "LINC01133"       "KCNJ10"          "ENSG00000225279" "ENSG00000227741" "ENSG00000258465" "ENSG00000228606" "ENSG00000234425"
 [722] "ENSG00000228863" "ENSG00000198358" "ENSG00000270149" "ENSG00000289121" "ARHGAP30"        "ENSG00000238934" "ENSG00000224985"
 [729] "TOMM40L"         "ENSG00000289106" "ENSG00000288670" "ENSG00000283696" "ENSG00000288093" "ENSG00000283360" "ENSG00000215840"
 [736] "ENSG00000273112" "ENSG00000226889" "ENSG00000229808" "ENSG00000285636" "ENSG00000254706" "ENSG00000272574" "CCDC190"        
 [743] "ENSG00000230739" "ENSG00000269887" "ENSG00000271917" "ENSG00000236206" "ENSG00000215838" "ENSG00000236364" "RPS3AP10"       
 [750] "ENSG00000230898" "ENSG00000229588" "ENSG00000225325" "LINC01675"       "LINC01363"       "ENSG00000272033" "ENSG00000287218"
 [757] "ENSG00000273160" "ADCY10"          "ENSG00000250762" "ENSG00000227722" "ENSG00000228697" "ENSG00000285622" "LINC00626"      
 [764] "ENSG00000235575" "ENSG00000213062" "ENSG00000288139" "LINC01681"       "LINC01142"       "ENSG00000225545" "ENSG00000235303"
 [771] "ENSG00000271811" "ENSG00000231424" "ENSG00000213060" "ENSG00000287336" "C1orf105"        "ENSG00000224228" "ENSG00000231615"
 [778] "ENSG00000238272" "ENSG00000285777" "ENSG00000289426" "KLHL20"          "ENSG00000225591" "RN7SKP160"       "Y-RNA.50"       
 [785] "ENSG00000237249" "ENSG00000289425" "RNU6-307P"       "ENSG00000287697" "KIAA0040"        "ENSG00000260990" "LINC02803"      
 [792] "ENSG00000236021" "ENSG00000227815" "ENSG00000286754" "LINC01645"       "ENSG00000227579" "ENSG00000213058" "CLEC20A"        
 [799] "C1orf220"        "ENSG00000273384" "FAM20B"          "ENSG00000212338" "ENSG00000225711" "ENSG00000243062" "LINC02818"      
 [806] "ENSG00000272906" "RN7SL230P"       "ENSG00000261831" "ENSG00000260360" "CEP350"          "ENSG00000261817" "LINC02816"      
 [813] "ENSG00000243155" "ENSG00000251520" "LINC01732"       "LINC01699"       "ENSG00000288574" "ENSG00000272880" "ENSG00000287452"
 [820] "ENSG00000224810" "LINC01344"       "ENSG00000287808" "ENSG00000287929" "ENSG00000286372" "ENSG00000289581" "ENSG00000286655"
 [827] "ENSG00000230470" "ENSG00000285847" "ENSG00000286378" "ENSG00000233583" "ENSG00000238061" "ENSG00000279838" "ENSG00000273004"
 [834] "ENSG00000261729" "ENSG00000273198" "ENSG00000228238" "ENSG00000288562" "LINC01036"       "ENSG00000285894" "ENSG00000238054"
 [841] "LINC01035"       "LINC01701"       "ENSG00000287472" "ENSG00000230987" "ENSG00000225811" "LINC01351"       "ENSG00000241505"
 [848] "LINC01720"       "ENSG00000285638" "LINC02770"       "ENSG00000285280" "ENSG00000236069" "ZNF101P2"        "RO60"           
 [855] "LINC01031"       "ENSG00000286285" "ENSG00000227240" "ENSG00000285718" "FAM204BP"        "ENSG00000224901" "ENSG00000287989"
 [862] "ENSG00000261573" "LINC01222"       "LINC01221"       "ENSG00000286541" "LINC02789"       "LINC00862"       "ENSG00000230623"
 [869] "ENSG00000282849" "ENSG00000229191" "ENSG00000224536" "RPS10P7"         "ENSG00000236390" "ENSG00000223774" "ENSG00000232626"
 [876] "ENSG00000226862" "ENSG00000235449" "ENSG00000234775" "ENSG00000224671" "NPM1P40"         "LINC01353"       "ENSG00000288925"
 [883] "LINC01136"       "ENSG00000227417" "ENSG00000286383" "ENSG00000286572" "ENSG00000219133" "ENSG00000226330" "ENSG00000288934"
 [890] "ENSG00000240710" "ENSG00000287197" "ENSG00000229657" "SNRPGP10"        "ENSG00000236942" "ENSG00000285521" "ENSG00000286619"
 [897] "PM20D1"          "ENSG00000285417" "ENSG00000226780" "ENSG00000236889" "ENSG00000261000" "ENSG00000234981" "ENSG00000279946"
 [904] "ENSG00000224114" "IL10"            "ENSG00000275392" "ENSG00000237074" "ENSG00000283044" "ENSG00000286198" "ENSG00000232537"
 [911] "G0S2"            "ENSG00000287343" "ENSG00000287354" "ENSG00000279333" "ENSG00000284299" "ENSG00000284376" "ENSG00000287033"
 [918] "LINC00467"       "SLC30A1"         "ENSG00000288738" "LINC01693"       "ENSG00000231057" "ENSG00000226868" "ENSG00000229983"
 [925] "LINC02608"       "ENSG00000230063" "ENSG00000234915" "ENSG00000287445" "LINC02771"       "ENSG00000286213" "ENSG00000260805"
 [932] "LINC02773"       "ENSG00000236905" "ENSG00000236317" "ENSG00000225233" "ENSG00000228255" "LINC00538"       "ENSG00000274895"
 [939] "ENSG00000272167" "LINC02775"       "ENSG00000228470" "ENSG00000287008" "ENSG00000229242" "LINC00210"       "LINC01653"      
 [946] "ENSG00000223375" "LINC02869"       "ENSG00000277007" "ENSG00000287676" "SLC30A10"        "ENSG00000286231" "LINC01352"      
 [953] "LINC02817"       "DUSP10"          "LINC01655"       "LINC02257"       "LINC02474"       "LINC01705"       "ENSG00000236230"
 [960] "ENSG00000276997" "ENSG00000267305" "ENSG00000272750" "ENSG00000288824" "ENSG00000287684" "ENSG00000287338" "ENSG00000226601"
 [967] "ENSG00000288999" "GTF2IP20"        "ENSG00000278467" "ENSG00000232628" "ENSG00000237101" "ENSG00000229742" "ENSG00000286174"
 [974] "LINC02813"       "ENSG00000286719" "LINC02765"       "ENSG00000282418" "ENSG00000289602" "ENSG00000227496" "ENSG00000226349"
 [981] "ENSG00000242861" "ENSG00000255835" "ENSG00000289341" "LINC01703"       "ENSG00000275406" "ENSG00000287627" "ENSG00000288674"
 [988] "ENSG00000287532" "ENSG00000228625" "ENSG00000236636" "ENSG00000286389" "LINC02809"       "ENSG00000280157" "ENSG00000269934"
 [995] "ENSG00000270110" "ENSG00000286773" "ENSG00000270104" "ENSG00000231563" "ENSG00000279306" "ENSG00000206878"
 [ reached getOption("max.print") -- omitted 11705 entries ]

# miQC is designed to be run with the Bioconductor package scater, which has a built-in function addPerCellQC to calculate basic QC metrics like number of unique genes detected per cell and total number of reads. When we pass in our list of mitochondrial genes, it will also calculate percent mitochondrial reads.

sce <- addPerCellQC(sce, subsets = feature_ctrls)
head(colData(sce))

DataFrame with 6 rows and 11 columns
                          orig.ident nCount_RNA nFeature_RNA  pct_mito    ident       sum  detected subsets_mito_sum subsets_mito_detected
                            <factor>  <numeric>    <integer> <numeric> <factor> <numeric> <integer>        <numeric>             <integer>
7316-371:AAACCCAAGAGCGACT   7316-371       1875         1305     0.000 7316-371      1875      1305              130                    95
7316-371:AAACCCAAGATCGCCC   7316-371       2960         1771     0.034 7316-371      2960      1771              254                   175
7316-371:AAACCCACAAGCTCTA   7316-371        809          596     0.000 7316-371       809       596               61                    51
7316-371:AAACCCACACAACATC   7316-371       4000         2212     0.000 7316-371      4000      2212              291                   200
7316-371:AAACCCACACACAGCC   7316-371       1701         1202     0.000 7316-371      1701      1202              152                   110
7316-371:AAACGAAAGAGAGAAC   7316-371       3225         1913     0.000 7316-371      3225      1913              237                   160
                          subsets_mito_percent     total
                                     <numeric> <numeric>
7316-371:AAACCCAAGAGCGACT              6.93333      1875
7316-371:AAACCCAAGATCGCCC              8.58108      2960
7316-371:AAACCCACAAGCTCTA              7.54017       809
7316-371:AAACCCACACAACATC              7.27500      4000
7316-371:AAACCCACACACAGCC              8.93592      1701
7316-371:AAACGAAAGAGAGAAC              7.34884      3225

miQC

print(plotMetrics(sce))

model <- mixtureModel(sce)
parameters(model)

                       Comp.1      Comp.2
coef.(Intercept) 7.8486196586 4.735002213
coef.detected    0.0001542046 0.001813925
sigma            0.7421263579 1.399036934

head(posterior(model))

          [,1]      [,2]
[1,] 0.5027739 0.4972261
[2,] 0.7401113 0.2598887
[3,] 0.8518726 0.1481274
[4,] 0.7170284 0.2829716
[5,] 0.8084498 0.1915502
[6,] 0.6790415 0.3209585

# Plot
# The cells at the very top of the graph are almost certainly compromised, most likely to have been derived from the distribution with fewer unique genes and higher baseline mitochondrial expression.
print(plotModel(sce, model))

# Attention: To add code for cases when miQC won't work
# https://github.com/AlexsLemonade/scpca-nf/blob/main/bin/filter_sce.R

Filter data by min_genes

# Plot
print(plotFiltering(sce, model))

# Filter and remove the indicated cells by miQC
filtered_sce <- filterCells(sce, model)

Removing 837 out of 3564 cells.

# Convert sce back to seurat object 
seurat_obj <- as.Seurat(filtered_sce, 
                        counts = "counts",
                        data = "logcounts")
  
# Filter data by min_genes 
seurat_obj <- subset(seurat_obj, subset = nFeature_RNA > min_genes)

# How many cells are in the seurat object after filtering?
cells_so_after_filter_num <- ncol(seurat_obj)

There are 2727 cells in the seurat object after filtering.

Number of cells express a specific gene

# For each gene, calculate % of cells expressing it and the mean
pct_cells <- apply(as.data.frame(seurat_obj@assays$RNA@counts), 1, function(x) (sum(x != 0))) / ncol(seurat_obj@assays$RNA@counts)

gene_means <- rowMeans(as.data.frame(seurat_obj@assays$RNA@counts))

p <- as.data.frame(cbind(pct_cells, gene_means)) %>% 
  rownames_to_column("gene")

# Set seed
set.seed(2023)

ggplot(data = p, aes(x = gene_means, y = pct_cells, label = gene)) +
  geom_point(size = 3, alpha = 0.6) +
  xlab("Mean count per gene") +
  ylab("Percent cells expressing gene") +
  stat_dens2d_labels(geom = "text_repel", keep.fraction = 0.001)

Number of expressed genes in the library

# number of expressed genes
num_genes <- apply(as.data.frame(seurat_obj@assays$RNA@counts), 2, function(x) (sum(x != 0)))

libsize <- colSums(as.data.frame(seurat_obj@assays$RNA@counts))

l  <- as.data.frame(cbind(num_genes, libsize)) 

# Set seed
set.seed(2023)

ggplot(data = l, aes(x = log10(libsize), y = num_genes)) +
  geom_point(size = 3, alpha = 0.6) +
  xlab("Library Size (log10)") +
  ylab("Number of Expressed Genes") 

Plot data after filter low quality cells

# plot number of genes
genes_sing <- plot_distribution(seurat_obj,
                                features = "nFeature_RNA",
                                grouping = grouping) +
  geom_hline(yintercept = min_genes, color = "#D41159", size = 2) +
  #geom_hline(yintercept = max(seurat_obj@meta.data$nFeature_RNA), color = "#D41159", size = 2) +
  scale_fill_manual(values = alpha(c("#10559a"), .4)) +
  #annotate(geom="text", x = 0.7, y = max(seurat_obj@meta.data$nFeature_RNA), label= glue(""), color="#D41159") +
  annotate(geom="text", x = 0.7, y = min_genes - 200, label=glue("Min genes: {min_genes}"), color="#D41159")

Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.

# plot number of UMIs
umi_sing <- plot_distribution(seurat_obj,
                              features = "nCount_RNA",
                              grouping = grouping) +
    scale_fill_manual(values = alpha(c("#10559a"), .4))

Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.

# plot percent mitochondrial reads
mito_sing <- plot_distribution(seurat_obj,
                               features = "pct_mito",
                               grouping = grouping) +
  #geom_hline(yintercept = max(seurat_obj@meta.data$pct_mito), color = "#D41159", size = 2) +
  scale_fill_manual(values = alpha(c("#10559a"), .4)) 

Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.

  #annotate(geom="text", x = 0.7, y = max(seurat_obj@meta.data$pct_mito), label = glue(""), color="#D41159")

# get the legend for one of the plots to use as legend for the combined plot
legend_grid <- get_legend(mito_sing)

# Combine plots
plot_grid(genes_sing + theme(legend.position = "none"),
          umi_sing + theme(legend.position = "none"),
          mito_sing + theme(legend.position = "none"),
          legend_grid,
          ncol = 4)

Normalize data, find variable features, and scale the data

# normalize seurat object using specified method, on specified assay
seurat_obj <- NormalizeData(seurat_obj, normalization.method = "LogNormalize",
                             nfeatures = 2000, assay = "RNA")

Warning: The following arguments are not used: nfeatures

seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = "vst", nfeatures = 2000) 

Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

seurat_obj <- ScaleData(seurat_obj, features = VariableFeatures(seurat_obj))

Centering and scaling data matrix

Run PCA

We will run PCA to reduce dimensionality of the matrix (as defined in the params).

# run PCA on specified assay using the number of PCs specified
seurat_obj <- run_dr(data = seurat_obj,
                     dr_method = "pca",
                     var_features = TRUE,
                     assay = assay,
                     num_pcs = num_pcs,
                     prefix =  prefix)

Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from PClognorm to PClognorm_

Run UMAP

We will run UMAP based on the previous estimated PCs and for a variety of combinations as for the number of dimensions and neighbors (as defined in the params) to explore the clustering of each sample in the library.

# Create a dataframe of all of the possible combinations of number of PCs 
# to use for UMAP, and the number of neighbors
num_dim_vect <- c(num_dim)
num_neighbors_vect <- c(num_neighbors)
possibilities <- expand.grid(num_dim_vect, num_neighbors_vect)

# For each of these combinations, calculate UMAP
for(i in 1:nrow(possibilities)) {
  num_dim <- possibilities[i, 1]
  num_neighbors <- possibilities[i, 2]
  seurat_obj <- run_dr(data = seurat_obj, dr_method = "umap", reduction = paste0("pca", prefix),
                     num_dim_use = num_dim, assay = "RNA", num_neighbors = num_neighbors,
                     prefix = glue("ndim{num_dim}nn{num_neighbors}{prefix}"))
}

Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAPndim20nn30lognorm to UMAPndim20nn30lognorm_Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAPndim25nn30lognorm to UMAPndim25nn30lognorm_Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAPndim20nn20lognorm to UMAPndim20nn20lognorm_Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAPndim25nn20lognorm to UMAPndim25nn20lognorm_Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAPndim20nn10lognorm to UMAPndim20nn10lognorm_Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by ‘spam’
Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAPndim25nn10lognorm to UMAPndim25nn10lognorm_

# Generate metadata
reduction_names <- c(paste0("umap", "ndim", possibilities[,1], "nn", possibilities[,2], prefix), paste0("pca", prefix)) # Export the reductions to Seurat

metadata <- as_data_frame_seurat(seurat_obj, reduction = reduction_names,
                                 metadata = TRUE)

Plot UMAP

# plot the first two PCs, and all of the different UMAPs
reduction_names <- c(paste0("UMAP", "ndim", possibilities[,1], "nn", possibilities[,2], prefix), paste0("PC", prefix))

plot_dr <- data.frame(X = paste0(reduction_names, "_1"),
                      Y = paste0(reduction_names, "_2"),
                      stringsAsFactors = FALSE)

for(i in 1:nrow(plot_dr)){
  print(current_plot <- plot_scatter(metadata = metadata,
                                     scratch_dir,
                                     proj_name = sample_name,
                                     log_file = log_file,
                                     X = plot_dr[i,1],
                                     Y = plot_dr[i,2],
                                     color = grouping,
                                     write = TRUE))
  
  print(current_plot <- plot_scatter(metadata = metadata,
                                     scratch_dir,
                                     proj_name = sample_name,
                                     log_file = log_file,
                                     X = plot_dr[i,1],
                                     Y = plot_dr[i,2],
                                     color = "nFeature_RNA",
                                     write = TRUE))
    
  print(current_plot <- plot_scatter(metadata = metadata,
                                     scratch_dir,
                                     proj_name = sample_name,
                                     log_file = log_file,
                                     X = plot_dr[i,1],
                                     Y = plot_dr[i,2],
                                     color = "nCount_RNA",
                                     write = TRUE))
    
  print(current_plot <- plot_scatter(metadata = metadata,
                                     scratch_dir,
                                     proj_name = sample_name,
                                     log_file = log_file,
                                     X = plot_dr[i,1],
                                     Y = plot_dr[i,2],
                                     color = "pct_mito",
                                     write = TRUE))
}

Save filtered metadata and Seurat object

Let’s save the filtered seurat object to be used for further downstream analysis (seurat_obj.rds).

# Save metadata
write_tsv(metadata, path = glue("{results_dir}/metadata_create_{sample_name}.tsv"))

Warning: The `path` argument of `write_tsv()` is deprecated as of readr 1.4.0.
Please use the `file` argument instead.

# Save Seurat object
saveRDS(seurat_obj, file = paste0(results_dir, "/", "seurat_obj.rds"))

R version 4.3.1 (2023-06-16)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] scooter_0.0.0.9004          testthat_3.2.0              irlba_2.3.5.1               Matrix_1.6-3                SeuratWrappers_0.2.0       
 [6] flexmix_2.3-19              lattice_0.22-5              SeuratObject_5.0.1          Seurat_4.4.0                scater_1.30.0              
[11] scuttle_1.12.0              SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0 Biobase_2.62.0              GenomicRanges_1.54.1       
[16] GenomeInfoDb_1.38.1         IRanges_2.36.0              S4Vectors_0.40.1            BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[21] matrixStats_1.1.0           miQC_1.10.0                 ggrepel_0.9.4               ggpmisc_0.5.5               ggpp_0.5.5                 
[26] stringr_1.5.1               GGally_2.1.2                ggplot2_3.4.4               forcats_1.0.0               devtools_2.4.5             
[31] usethis_2.2.2               cowplot_1.1.1               future_1.33.0              

loaded via a namespace (and not attached):
  [1] fs_1.6.3                  spatstat.sparse_3.0-3     bitops_1.0-7              httr_1.4.7                RColorBrewer_1.1-3       
  [6] ggsci_3.0.0               profvis_0.3.8             tools_4.3.1               sctransform_0.4.1         utf8_1.2.4               
 [11] R6_2.5.1                  lazyeval_0.2.2            uwot_0.1.16               urlchecker_1.0.1          withr_2.5.2              
 [16] sp_2.1-1                  prettyunits_1.2.0         gridExtra_2.3             progressr_0.14.0          textshaping_0.3.7        
 [21] quantreg_5.97             cli_3.6.1                 spatstat.explore_3.2-5    labeling_0.4.3            sass_0.4.7               
 [26] spatstat.data_3.0-3       readr_2.1.4               ggridges_0.5.4            pbapply_1.7-2             systemfonts_1.0.5        
 [31] parallelly_1.36.0         sessioninfo_1.2.2         rstudioapi_0.15.0         FNN_1.1.3.2               generics_0.1.3           
 [36] vroom_1.6.4               ica_1.0-3                 spatstat.random_3.2-1     dplyr_1.1.4               ggbeeswarm_0.7.2         
 [41] fansi_1.0.5               abind_1.4-5               lifecycle_1.0.4           yaml_2.3.7                SparseArray_1.2.2        
 [46] Rtsne_0.16                grid_4.3.1                promises_1.2.1            crayon_1.5.2              miniUI_0.1.1.1           
 [51] beachmat_2.18.0           pillar_1.9.0              knitr_1.45                future.apply_1.11.0       codetools_0.2-19         
 [56] leiden_0.4.3.1            glue_1.6.2                data.table_1.14.8         remotes_2.4.2.1           vctrs_0.6.4              
 [61] png_0.1-8                 spam_2.10-0               gtable_0.3.4              cachem_1.0.8              xfun_0.41                
 [66] S4Arrays_1.2.0            mime_0.12                 survival_3.5-7            ellipsis_0.3.2            fitdistrplus_1.1-11      
 [71] ROCR_1.0-11               nlme_3.1-163              bit64_4.0.5               RcppAnnoy_0.0.21          rprojroot_2.0.4          
 [76] bslib_0.5.1               vipor_0.4.5               KernSmooth_2.23-22        colorspace_2.1-0          nnet_7.3-19              
 [81] tidyselect_1.2.0          processx_3.8.2            bit_4.0.5                 compiler_4.3.1            BiocNeighbors_1.20.0     
 [86] SparseM_1.81              desc_1.4.2                DelayedArray_0.28.0       plotly_4.10.3             scales_1.2.1             
 [91] lmtest_0.9-40             callr_3.7.3               digest_0.6.33             goftest_1.2-3             spatstat.utils_3.0-4     
 [96] rmarkdown_2.25            XVector_0.42.0            htmltools_0.5.7           pkgconfig_2.0.3           sparseMatrixStats_1.14.0 
[101] fastmap_1.1.1             rlang_1.1.2               htmlwidgets_1.6.2         shiny_1.8.0               DelayedMatrixStats_1.24.0
[106] farver_2.1.1              jquerylib_0.1.4           zoo_1.8-12                jsonlite_1.8.7            BiocParallel_1.36.0      
[111] BiocSingular_1.18.0       RCurl_1.98-1.13           magrittr_2.0.3            polynom_1.4-1             modeltools_0.2-23        
[116] GenomeInfoDbData_1.2.11   dotCall64_1.1-0           patchwork_1.1.3           munsell_0.5.0             Rcpp_1.0.11              
[121] viridis_0.6.4             reticulate_1.34.0         stringi_1.8.1             brio_1.1.3                zlibbioc_1.48.0          
[126] MASS_7.3-60               plyr_1.8.9                pkgbuild_1.4.2            parallel_4.3.1            listenv_0.9.0            
[131] deldir_1.0-9              splines_4.3.1             tensor_1.5                hms_1.1.3                 ps_1.7.5                 
[136] igraph_1.5.1              spatstat.geom_3.2-7       reshape2_1.4.4            ScaledMatrix_1.10.0       pkgload_1.3.3            
[141] evaluate_0.23             BiocManager_1.30.22       tzdb_0.4.0                httpuv_1.6.12             MatrixModels_0.5-3       
[146] RANN_2.6.1                tidyr_1.3.0               purrr_1.0.2               polyclip_1.10-6           reshape_0.8.9            
[151] scattermore_1.2           rsvd_1.0.5                xtable_1.8-4              later_1.3.1               viridisLite_0.4.2        
[156] ragg_1.2.6                tibble_3.2.1              memoise_2.0.1             beeswarm_0.4.0            cluster_2.1.4            
[161] globals_0.16.2